the new lot
calculate the mean for each measured parameter on the new lot. calculate indices from the
assay values on a new lot of control should be confirmed before it is put into routine use. test the new lot when the instrument is in good working order and quality control results on the old lot are acceptable. the laboratory recovered mean should be within the assay range .
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for facility 2, where 4 previously scheduled demands had to be left - shifted to make space for the new demand .
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since there was not a sufficiently long gap in the current schedule to allocate the entire production for the new demand, one possibility to create a feasible schedule may be to shift previously - scheduled demands to an earlier time to make space for the new demand. thereby, the heuristic identifies the latest gap in the considered facility before the due date. all conflicting scheduled demands before this gap are shifted backward in time (towards the start of the planning horizon), without changing the order, and just enough to make space for the new demand. this can be seen in fig .
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variations of construction heuristics have been used for various types of lot sizing problems. for example, ho et al. (
bitran, g. r. , yanasse, h. h. : computational complexity of the capacitated lot size problem. manag. sci .
wagner, h. m. , whitin, t. m. : dynamic version of the economic lot size model. manag. sci .
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drexl, a. , kimms, a. : lot sizing and scheduling–survey and extensions. eur. j. oper. res .
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another option to fit the demand may be to split the new demand. in this alternative, the heuristic will again consider the latest gap before the due date, and use all available consecutive capacity. then, it will attempt to schedule the rest of the demand at each of the other facilities, but only considering options
, removes a demand and re - inserts it at a new random position. the algorithm is run for 1500 generations, and all results are based on averages over 50 runs .
karimi, b. , fatemi ghomi, s. , wilson, j. : the capacitated lot sizing problem: a review of models and algorithms. omega
the production of biopharmaceuticals is an expensive and time - consuming endeavour. the average cost to bring a new biopharmaceutical to market is estimated at $ 1. 2–1. 8 billion given the high attrition rates (dimasi and grabowski
almeder, c. : a hybrid optimization approach for multi - level capacitated lot - sizing problems. eur. j. oper. res .
jans, r. , degraeve, z. : modeling industrial lot sizing problems: a review. int. j. prod. res .
kimms, a. : a genetic algorithm for multi - level, multi - machine lot sizing and scheduling. comput. oper. res .
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the paper makes three contributions. first, it proposes a new heuristic to solve the multi - site biopharmaceutical lot sizing and scheduling problem that outperforms previously published approaches on a close to real - world case study. second, it demonstrates that the combination of genetic algorithm and construction heuristic that has been very successful in the scheduling domain can be successfully transferred to tackle complex lot sizing problems. third, it provides an example for the fact that an exact method based on typical simplifications (in this case fixed time periods) can be worse than a heuristic that does not need to make such simplifications .
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production planning aims to make best use of production resources in order to satisfy production goals or demand over a planning horizon. it is omnipresent in any manufacturing environment. one particular area in production planning is lot sizing and scheduling, which mostly focuses on the trade - off between set - up cost and inventory cost. the basic lot sizing problem was introduced in 1958 by wagner and whitin (
özdamar, l. , birbil, s. i. : hybrid heuristics for the capacitated lot sizing and loading problem with setup times and overtime decisions. eur. j. oper. res .
goren, h. c. , tunali, s. , jans, r. : a review of applications of genetic algorithms in lot sizing. j. intell. manuf .
jans, r. , degraeve, z. : meta - heuristics for dynamic lot sizing: a review and comparison of solution approaches. eur. j. oper. res .
a lot more work has been published on gas for the job - shop scheduling problem (jsp), and they typically use a permutation - based representation, and then apply a construction heuristic to actually construct the schedule based on the permutation (cheng et al .
james, r. j. w. , almada - lobo, b. : single and parallel machine capacitated lotsizing and scheduling: new iterative mip - based neighborhood search heuristics. comput. oper. res .
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). because of the simplifications required to model the problem for mixed integer linear programming, the solution potentially suffers from an artificial restriction of the search space. the goal of this paper is therefore to develop a more flexible metaheuristic approach for the biopharmaceutical lot sizing and scheduling problem, and contrast it with the proposed mixed - integer programming approach as described by lakhdar et al. (
visualisation of construction heuristic, based on a simple example with two facilities and four demands. items (i) — (vi) show the alternatives the heuristic considers when identifying the most profitable place to insert a new demand into the schedule. the rectangle representing a demand includes the setup time (so length varies depending on where the new demand is inserted). note that just using (i) or (ii), feasible options are found on facility 1 but not on facility 2. therefore the heuristic will terminate its search on facility 1 but continue on facility 2 using (iii), (iv), (v), and (vi )
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demands that are best scheduled just before the due date to save storage cost can be given a high priority. this will lead to the construction heuristic scheduling these demands early on, at a time where still a lot of capacity is available, and the cheapest option just before the due date would be selected .
), most meta - heuristics developed for lot sizing are validated only on artificial test data, failing to demonstrate that they can tackle the complexities of real - world problems. another current research gap is that the vast majority of work on lot sizing assumes that the problems are deterministic, whereas in reality, demand and production rates are usually subject to uncertainty. integrating uncertainty will require novel approaches, and eas have already demonstrated some promise in dealing with such problems (jin and branke
only if these alternatives are not feasible for a facility, e. g. , because a facility does not have a sufficiently large gap in its schedule, further options are explored that either move some of the already scheduled demands to make sufficient space for the new demand, split the demand into two parts and schedule the second part in another facility, or backlog the demand .
almada - lobo, b. , james, r. j. : neighbourhood search meta - heuristics for capacitated lot - sizing with sequence - dependent setups. int. j. prod. res .
, where the former appears much more often. in a direct representation, the sequence and lot sizes are directly encoded in the chromosome. the main challenge with such an approach is that mutations and crossovers can generate infeasible solutions, which is usually dealt with by discarding those solutions or by special repair operators (özdamar and birbil
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ho, j. c. , chang, y. l. , solis, a. o. : two modifications of the least cost per period heuristic for dynamic lot - sizing. j. oper. res. soc .
piperagkas, g. s. , konstantaras, i. , skouri, k. , parsopoulos, k. e. : solving the stochastic dynamic lot - sizing problem through nature - inspired heuristics. comput. oper. res .
silver, e. a. , meal, h. c. : a heuristic for selecting lot size quantities for the case of a deterministic time - varying demand rate and discrete opportunities for replenishment. prod. invent. manag .
the ratio in profit of the full model compared to the simple model, optimized by the ga, shown for multiples of the base market demand as presented in the case study. the increasing ratio indicates the increasing benefit the full model will have in more complicated scheduling problems than the basic case study. error bars represent the standard error
the above alternatives will be evaluated for all the facilities that are capable of producing the product. then, the demand is inserted into the schedule according to the most profitable alternative examined, and the algorithm moves on to schedule the next demand .
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, the demand is split, but rather than using the latest gap before the due date, the first part of the demand is scheduled in the earliest gap after the due date. the remaining portion of the demand is attempted again to be scheduled in all other facilities, but only using options
ramya, r. , rajendran, c. , ziegler, h. : capacitated lot - sizing problem with production carry - over and set - up splitting: mathematical models. int. j. prod. res .
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), who considered the case of a single product with deterministic demand. since then, many different extensions have been considered, reflecting the different environments in different industries. a particularly important extension is to include capacity constraints, resulting in the “capacitated lot sizing problem” (clsp). good overviews on the research in this area have been compiled, for example, by drexl and kimms (
), which generates active schedules by iteratively selecting the job with the highest priority (lowest permutation index) from the set of eligible jobs, and then scheduling it at the earliest possible time. however, this approach cannot be directly transferred to our lot sizing problem, because (i) scheduling as early as possible would lead to excessive storage costs and (ii) the existence of a heterogeneous set of alternative facilities .
pitakaso, r. , almeder, c. , doerner, k. f. , hartl, r. f. : a max - min ant system for unconstrained multi - level lot - sizing problems. comput. oper. res .
). its task is to schedule demands (or rather the production to satisfy the demand, but for simplicity we will continue calling it demand) sequentially, in the order prescribed by the ga. when deciding at what time and what facility to insert a new demand into the schedule, the heuristic explores a number of different alternatives, and then greedily picks the alternative that creates the smallest additional cost. in essence, the heuristic will consider each facility in which the product may be produced. it then tries to schedule the entire demand in an uninterrupted way as late as possible to minimise storage cost, and as late as possible but adjacent to already scheduled demand of the same product to avoid setup cost and time .
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almada - lobo, b. , klabjan, d. , antónia carravilla, m. , oliveira, j. f. : single machine multi - product capacitated lot sizing with sequence - dependent setups. int. j. prod. res .
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in the following two subsections, we first explain the proposed construction heuristic, then provide details on the ga used .
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. the gantt chart of the milp solution generally shows shorter campaigns (sequences of batches of the same product), and, especially visible on facility i4, small gaps between production in different time periods, simply because the time period (of 90 days) is not equivalent to a duration spanned by a multiple of batches for this product in this facility. the schedule optimised by the ga has longer un - interrupted idle time, which may be advantageous if a new product is introduced to the facility or if a third party is seeking to rent and use production capacity .
. the lead time allows for the duration of the first batch of a campaign plus the setup and cleaning time before the first batch commences. incorporation of lead time is enforced by a binary variable
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by the smaller rectangle for the scheduled demand). in order to reduce the magnitude of the penalty, the heuristic will schedule the demand as early as possible in a gap that either straddles, or is later than, the due date. an example is provided in fig .
mehdizadeh, e. , hajipour, v. , mohammadizadeh, m. r. : a bi - objective multi - item capacitated lot - sizing model: two pareto - based meta - heuristic algorithms. int. j. manag. sci. eng. manag .
biopharmaceutical manufacturing requires high investments and long - term production planning. for large biopharmaceutical companies, planning typically involves multiple products and several production facilities. production is usually done in batches with a substantial set - up cost and time for switching between products. the goal is to satisfy demand while minimising manufacturing, set - up and inventory costs. the resulting production planning problem is thus a variant of the capacitated lot - sizing and scheduling problem, and a complex combinatorial optimisation problem. inspired by genetic algorithm approaches to job shop scheduling, this paper proposes a tailored construction heuristic that schedules demands of multiple products sequentially across several facilities to build a multi - year production plan (solution). the sequence in which the construction heuristic schedules the different demands is optimised by a genetic algorithm. we demonstrate the effectiveness of the approach on a biopharmaceutical lot sizing problem and compare it with a mathematical programming model from the literature. we show that the genetic algorithm can outperform the mathematical programming model for certain scenarios because the discretisation of time in mathematical programming artificially restricts the solution space .
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for greater control sensitivity each laboratory should establish its own mean and acceptable range and periodically reevaluate the mean. the laboratory range may include values outside of the assay range. target values not listed on the assay sheet may be established by the user if the control is suitable for the method .
the quality of the solution produced by the above construction heuristic is to some extent dependent on the order in which the demands are inserted into the schedule since available production capacity is more restricted the later a demand is considered. by giving priority to certain demands mainly three situations can be created .
). specifically, each demand is given a unique id number (from 1 to 225), and the chromosome is a permutation of these numbers. the ordering of the numbers on the chromosome determines the order by which the construction heuristic processes the respective demands (from first to last position) and thus influences the resulting schedule .
overall, from these results, we conclude that the suggested ga approach is competitive with the milp approach, but does not suffer from the introduction of artificial time periods and thus is sometimes able to find better solutions than milp. the trend seems to be that the times for both optimisation methods are going up linearly. however the increase of the ga approach is of a smaller factor than that of the milp optimisations. this suggests that the relative performance of the ga is less susceptible to the detrimental impact of increasing the scale of the problem .
calculate control limits from the laboratory mean and long - term standard deviation. the longterm
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). to compute the fitness value of a chromosome, a construction scheme is called, which constructs the solution backwards, starting from the end of the planning horizon .
in terms of their impact on profit. it shows the ratio of the obtained profit depending on whether the construction heuristic during the ga search was limited to looking at alternatives
also reports on the runtime of the two algorithms. for milp, the stopping criterion was 600s, so the runtime remained the same, but the optimality gap increased as the problem became more difficult by increasing the demand and thus utilisation level. the ga was run for a fixed number of generations. the computational time still increased with increasing the demand level. the reason is that an increasing demand raises the utilisation level and the construction heuristic is then less likely to be able to schedule a demand in steps (i) or (ii), and thus more often has to look at the other alternatives for scheduling it. this will be explored further in the next subsection .
if the facilities are really busy, it may be best (or the only feasible option) to backlog the demand. that is to say that the time slot allocated to produce the material to meet the demand falls partly or wholly later than the due date for the demand. as described in the case study, this will result in a monetary penalty and part of the demand being lost, as is reflected in fig .
, due to different possibilities in scheduling the remaining part of a demand in case of a split. this means that in the worst case the complexity of the construction heuristic is
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describes in more detail the case study used to evaluate our approach. the ga and the associated construction heuristic are explained in sect .
sd is the average sd from previous lots. the sd for each parameter should be very
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shows the convergence of the ga over generations, and compares it with a purely random search, using fully random permutations or limited random permutations, i. e. , when half of the permutations are only random amongst demands of the same year, but the order on years is kept. as can be seen the results optimised by the ga are considerably better than the results obtained by random search. the limited randomisation helps in particular for the less loaded problems (1
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the appendix summarises the mathematical programme used in this paper and introduced by lakhdar et al. (
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examines how often the various alternatives to insert a demand are actually selected by the construction heuristic, averaged over the best solution found in each of the 50 runs. as can be seen in the table, in the standard case, the majority of demands (92. 8 %) are inserted by either scheduling it as late as possible
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denote individual facilities, products, and time periods respectively. the subsets characterising the facilities being considered are :
biopharmaceutical production is typically done in a batch - wise manner, with substantial set - up cost and time for switching between products, and a relatively high storage cost. the resulting problem can thus be considered as a variant of the lot - sizing and scheduling problem, where a “lot” (or “campaign” as it is often called in this industry) is composed of a set of batches. however, biopharmaceutical production has a number of characteristics that make it challenging to optimise. to spread risk, companies usually have a portfolio of various products, and manufacturing takes place across a network of different facilities, including in - house facilities and outsourced (contract) manufacturing. the facilities’ capabilities usually vary with respect to the set of products they can produce, the production rates for different products, batch production costs and batch production times. furthermore, products have a finite shelf - life and cannot be stored for very long. overall, biopharmaceutical production constitutes a complex combinatorial optimisation problem .
we also investigated the scaling behaviour of both optimisation methods with increasing problem sizes. for this, we ran the ga and milp for problems with longer time horizons of 23 and 30 years (in addition to the 15 year - long base case). for the longer time horizons, the demand forecasts for the years after year 15 were set equal to the forecast for each product in year 15. to compare the two methods, the milp was first run for all three problem sizes with a stopping criterion of a 0. 25% optimality gap, at which point, the solution quality (profit) was recorded along with the time taken to achieve the solution. this profit value was then used as a target for the ga. the average time over the 50 runs that it took for the ga to match or beat those targets was recorded. the results of this experiment are shown in table
. this is reassuring, since if such an insertion is possible, the other options are not tested, which significantly speeds up the algorithm. as we move to the scenarios with higher demand, the percentage drops from 92. 8 to 78. 9% . this still constitutes the majority of cases, but clearly the other insertion alternatives of the heuristic become more important .
originally, we initialised individuals randomly, but then realised that better solutions are produced if demands from a single year are grouped together on the chromosome. unless stated otherwise, the results in this paper are thus based on runs where 50% of the population is initialised randomly, whereas the other 50% only randomise the sequence of demands from the same year, but maintain the sequence of years (i. e. , all demands of a particular year appear in the permutation before the demands of later years). for fitness evaluation, the ga calls the construction heuristic described in sect .
in this paper, we have considered the lot sizing and scheduling problem for a complex biopharmaceutical production scenario featuring multiple products, multiple facilities, and batch processing. for this challenging optimisation problem, we have proposed an ga based on an indirect permutation encoding that is decoded into a full schedule by a novel construction heuristic tailored to the problem at hand. a comparison with an milp approach from the literature showed that the ga is at least competitive, and often produces even better results than the milp approach. the reason is that the milp model artificially imposes a time granularity by dividing the time into discrete periods that is not needed in the ga approach. this shows that although gas are heuristic methods, they can sometimes outperform exact methods not only in terms of running time, but also because they are able to work with a model closer to reality .
to avoid setup times and setup costs, it may be beneficial to schedule a demand adjacent to the same product already scheduled. the heuristic picks the latest time slot before the due date that allows to link to a previously - scheduled demand of the same product, and has sufficient available capacity to schedule the entire demand–see fig .
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. the results of the empirical evaluation, including a comparison with an milp approach, are reported in sect .
runtimes strongly depend on the implementation skills of the developer, the hardware used, and software tools used, and thus have to be handled with caution. nonetheless, table
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), the ga solution has lower manufacturing costs, i. e. , utilises better the low - cost facilities, and lower storage costs. it also manages to satisfy all the demand (customer service level of 100 %), whereas the milp model chooses to backlog some of the demand. this is because the ga tries to satisfy all the demand as first priority and only backlogs if there is no other feasible option. the milp, however, has an explicit trade - off between backlog and other costs, and backlogs if the resulting solution has a higher profit. on the other hand, the setup costs of the ga solution are higher. overall, the profit generated by the ga solution is consistently higher, and by more than the 0. 25% optimality gap, i. e. the difference between the best solution found and the upper bound determined by the milp solver. this is possible because milp, due to its imposed time granularity, has an artificially restricted search space. it can switch less often between products, resulting in lower setup cost and higher storage cost. also, it sometimes wastes part of a time period, which may mean the need to use occasionally more expensive facilities, resulting in higher manufacturing costs. these differences can be seen also by comparing the gantt charts of the optimal solutions found by the milp and the ga which are depicted in fig .
( denoted as “simple”), or all alternatives (“full”). a profit ratio of 1 means that the two models obtain the same profit, while a greater profit ratio indicates that the full model is able to achieve higher profits than the simple model. it confirms that the more complicated cases with splitting, backlogging and moving previously scheduled demand are responsible for an increasing share of the profit as the overall demand is increased. especially once the demand is increased to three times the original values, there seems to be a step change and the more complicated alternatives seem to become indispensable .
the objective is to maximize the overall profit, calculated as total revenue minus the cost for production, storage, setups and backlog penalties. given a set of heterogeneous facilities with different manufacturing yields, manufacturing cost, and batch production rates for different products, this takes into account maximizing the amount of products sold, and minimizing the manufacturing cost, the storage cost, the setup cost, and any backlog penalty .
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demand. this also makes sense, as with higher utilisation of the facilities, there is increasing need to schedule demands outside the year the demand is delivered, and the artificial limitation of randomisation to within a year is no longer helpful .
in both individuals, this will also be true in the offspring. as the mutation operator we used shift mutation, which iterates through every element of the permutation and, with probability
for the scenario with twice the demand, the conclusions are similar to the base case. however, for three times the demand, it seems backlogging becomes crucial, and the milp approach seems better in doing that. while backlogging reduces the products sold due to lost demand and thus reduces revenue, the savings that can be achieved in terms of manufacturing cost and setup cost seem to outweigh this loss, and the overall profit of the milp approach is higher in this scenario. whether a slightly higher profit justifies a lower customer service level is a different issue. the ga’s construction heuristic, always tries to meet all the demand, even if this may lead to a possibly lower profit. finally, we observe that the ga has still lower storage cost and higher setup cost, probably due to not being constrained by the coarse time periods .
note that batch production means that unless the demand is exactly equal to an integer multiple of the batch size (which itself is different for different facilities) it is not possible to produce exactly the required demand. in such cases, the number of produced batches is always rounded up to the minimal integer number of batches necessary to fulfill the demand. the amount overproduced in such a case is put in storage, possibly to be used to (partly) fulfill future demand. before going through the steps above to insert a demand into the schedule, the construction heuristic will always check whether the product is in the storage, and try to partially fulfill the demand from storage. the cost associated with this is storage cost only, as manufacturing costs are invoked at the time of production, i. e. , when a previous scheduled demand produced that overcapacity. products left in storage that the heuristic cannot use in later steps are considered lost and have no value .
optimising this order is left to the ga, which was implemented in java using the ecj library (luke et al .
in order to better understand the importance and robustness of the various components of our algorithm, we did some additional experiments .
). it uses a permutation representation of all the demands to be scheduled, i. e. , 225 in the industrial case study used here (the number of elements in table
the first alternative considered is to schedule the entire demand as late as possible but before the due date, as one uninterrupted block, which minimizes storage cost at this facility. in the example, this is possible for facility 1, see fig .
the construction heuristic we propose in this paper is therefore tailored to our problem, and still allows us to use the permutation - based approach that is successful in the job shop scheduling domain .
the demand forecast comprises a time horizon of 15 years and 15 products (p1–p15). the forecast indicates yearly market demands, assumed to be fulfilled at the end of each year (table
the number of facilities. in practice, however, as we will show later, in the majority of cases, only options
) is the best possible, or that it is not possible to design an milp formulation that circumvents or at least reduces the impact of the time period length. however, the milp model is the only one we found in the literature for this problem, and we do believe that one of the advantages of gas is their greater flexibility in modelling the real world, and that solving a problem heuristically that is close to reality can sometimes work better than a model further from reality but solved to optimality .
) was 63, which is not consistent with the general trend of the other years, so we changed it to 163 .
we updated the units for setup cost, sales price, storage cost and backlog penalty to be consistent with the other data .
to this end, we design an genetic algorithm (ga) with an embedded problem - specific construction heuristic. the ga uses an indirect permutation encoding, i. e. , the specifically developed construction heuristic schedules demands sequentially in the order prescribed by the chromosome. as we demonstrate, the use of an ga allows for a more flexible and realistic model of the real - life problem and avoids some of the simplifications necessitated by available mathematical programming models .
the setup time includes the time of production of the first batch. in addition, it is assumed that a month is 30 days and, subsequently, a year is equal to 360 days .
the competition saw 11 finalists auctioning off a range of products which helped raise more that $ 11, 000 for the mcgrath foundation .
do not use the sd from parallel testing to establish a range. this sd is too narrow for control limits because it is obtained over a short time compared to the shelf life of the control .
. as can be seen, the time required for the milp and ga increases roughly linearly with problem size, however the factor by which runtime increases when moving from 15 to 30 years is 6. 6 for milp, but only 2. 8 for the ga .
this is a training film made for the british directorate of army psychiatry directed by carol reed and written by peter ustinov and eric ambler. the film features five different recruits and turns them into an efficient well motivated team. for fifty years this film was thought toe lost but this print turned up in india. the underlying plot of this training film formed the story line for the feature film the way ahead 1944
exemplary gantt chart of a schedule generated by the ga (top) and milp (bottom), for the standard case. the profit and customer service level (csl) for each schedule is also indicated
the first author would like to acknowledge funding through the epsrc centre for doctoral training in emergent macromolecular therapies, ep / l015218 / 1 .
which builds a schedule by inserting demands iteratively in the order prescribed by the solution’s chromosome. the actual fitness is then the overall profit of the resulting schedule, i. e. , revenue minus storage, production, setup cost and backlog penalty. this objective function is defined mathematically in the “appendix”. we did not spend much effort on tuning parameters to this problem, but experiments with different population sizes and mutation rates show that results are rather insensitive to the parameter settings (see table
) used mixed integer linear programming (milp) to solve this problem, and a full description of the mathematical programme formulation can be found in the “appendix”. the ga proposed in this paper is subject to the same constraints, except that we do not restrict production on a particular facility to one product per time period. this seemed to be an artificial restriction imposed only to reduce the modelling complexity of the milp .
for job shop scheduling, many successful gas use indirect encodings, with the ga only searching the space of permutations of jobs. for evaluation, a schedule is constructed from the permutation by a construction heuristic, often giffler - thompson, which iteratively selects the job with the highest priority (lowest permutation index) from the set of eligible jobs, and then schedules it at the earliest possible time. this avoids infeasible solutions and introduces a desirable heuristic bias, in the sense that it excludes obviously bad solutions (such as schedules with big gaps) from the search space. inspired by this work, we also propose using an indirect, permutation - based encoding combined with a construction heuristic. the construction heuristic, however, had to be carefully designed for the problem at hand .
for the ga, mean ± sd are listed, whereas milp is a deterministic method and was only run once. best mean is highlighted in bold–where the difference is not significant both are highlighted. ga timing values are the average time elapsed for each of the 50 runs (i. e. , the total runtime of 50 runs divided by 50 )
the new lot is a 1943 british drama film directed by carol reed and starring eric ambler , robert donat , kathleen harrison , bernard lee , raymond huntley , john laurie , peter ustinov and austin trevor , with music by richard addinsell . the film follows five new recruits from different background and their experiences as they join the army . the film was later expanded and remade as the way ahead , co-written by ambler and ustinov , directed by carol reed , and starring david niven . 
the new lot is a 1943 british drama film directed by carol reed and starring eric ambler, robert donat, kathleen harrison, bernard lee, raymond huntley, john laurie, peter ustinov and austin trevor, with music by richard addinsell. the film follows five new recruits from different background and their experiences as they join the army. the film was later expanded and remade as the way ahead, co-written by ambler and ustinov, directed by carol reed, and starring david niven.
the new lot is a 1943 british drama film directed by carol reed and starring eric ambler, robert donat, kathleen harrison, bernard lee, raymond huntley, john laurie, peter ustinov and austin trevor, with music by richard addinsell. the film follows five new recruits from different background and their experiences as they join the army. the film was later expanded and remade as the way ahead, co-written by ambler and ustinov, directed by carol reed, and starring david niven.