MIP models for production lot sizing problems with distribution costs and cargo arrangement

This study presents mixed integer programming (MIP) models for production lot sizing problems with distribution costs using unit load devices such as pallets and containers. Problems that integrate production lot sizing decisions and loading of the products in vehicles (bins) are also modelled, in which constraints such as weight limits, volume restrictions or the value of the cargo loaded in the bins are considered. In general, these problems involve a trade-off between production, inventory and distribution costs. Lot sizing decisions should take into account production capacity and product demand constraints. Distribution decisions are related to the loading and transport of products in unit load devices. The MIP models are solved by the branch-and-cut method of an optimization package and the results show that these approaches have the potential to address different practical situations.     

Safety Stock and Service Levels in Periodic Review Inventory Systems

The management of requirement (purchased parts and components) uncertainty in MRP systems is a serious problem for manufacturing firms. When manufacturing service levels fall below a predetermined service level, safety stock must be increased in order to adjust to the competitive environment. This paper presents the results of a set of simulation experiments in which we investigate some of the complexities of safety stock requirements, lot sizing performance and the marginal safety stock requirements associated with various service level policies. The results of the experiments have empirically shown that the marginal units of safety stock required for various service level policies follow a predictable (measurable) pattern. The research also indicates that the economic advantages of well-known dynamic lot sizing procedures are diminished by the presence of requirements uncertainty. Another important finding is the interaction between service level and ‘lumpy’ requirements. High levels of the coefficient of variation and the economic time between orders resulted in larger orders, reduced safety stock and high service levels. In general, the overall findings from this research will hopefully provide the decision maker with an in-depth understanding of the measurement and impact of various service level policies.     

Inventory control by different service levels

This paper examines the multiple period inventory control problem of a single product with multiple (two) prices, depending on service level, in which optimal pricing and ordering decisions are made in each period. Traditional inventory and pricing models consider only single products, single prices, and single service levels. However, this research paper finds that a seller can improve inventory control and revenue by offering multiple prices depending on service level. This research considers a single product with multiple (two) pricing policies corresponding to service level as follows: if the customer is willing to delay the shipment, he/she will be offered a lower regular price. Otherwise, the customer will pay the regular price plus extra charges for express service. In this paper, I show the following: (1) there is an optimal pricing and replenishment policy that can control inventory and (2) there exists a finite threshold for inventory levels such that if the inventory level at the beginning of each period is higher than the threshold, the customer will be offered the express service at the regular price, without any extra charge.     

The two-group heuristic to solve the multi-product,economic lot sizing and scheduling problem in flow shops

This paper presents a new and efficient heuristic to solve the multi-product, economic lot sizing and scheduling problem in flow shops. The problem addressed is that of making sequencing, lot sizing and scheduling decisions for a number of products so as to minimize the sum of setup costs, work-in-process inventory holding costs and final-products inventory holding costs while a given demand is fulfilled without backlogging. The proposed heuristic, called the two-group method (TG), assumes that the cycle time of each product is an integer multiple of a basic period and restricts these multiples to take either the value 1 or K where K is a positive integer. The products to be produced once each K basic period are then partitioned into K sub-groups and each sub-group is assigned to one and only one of the K basic periods of the global cycle. This method first determines a value for K and a feasible partition. Then, a production sequence is determined for each sub-group of products and a non-linear program is solved to determine lot sizes and a feasible schedule. We also show how to adapt our method to the case of batch streaming (transportation of sub-batches from one machine to the next). To evaluate its performance, the TG method was compared to both the common cycle method and a reinforced version of El-Najdawi’s job-splitting heuristic. Numerical results show that the TG method outperforms both of these methods.     

A possibilistic decision model for new product supply chain design

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a possibilistic SC configuration model for new products with unreliable or unavailable SC statistical data. The supply chain is modeled as a network of stages. Each stage may have one or more options characterized by the cost and lead-time required to fulfill required functions and may hold safety stock to prevent an inventory shortage. The objective is to determine the option and inventory policy for each stage to minimize the total SC cost and maximize the possibility of fulfilling the target service level. A fuzzy SC model is developed to evaluate the performance of the entire SC and a genetic algorithm approach is applied to determine near-optimal solutions. The results obtained show that the proposed approach allows decision makers to perform trade-off analysis among customer service levels, product cost, and inventory investment depending on their risk attitude. It also provides an alternative tool to evaluate and improve SC configuration decisions in an uncertain SC environment.     

Dynamic cost allocation for economic lot sizing games

We consider a cooperative game defined by an economic lot sizing problem with concave ordering costs over a finite time horizon, in which each player faces demand for a single product in each period and coalitions can pool orders. We show how to compute a dynamic cost allocation in the strong sequential core of this game, i.e. an allocation over time that exactly distributes costs and is stable against coalitional defections at every period of the time horizon.     

Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches

Proofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search, genetic algorithms and simulated annealing, have become popular and efficient tools for solving hard combinatorial optimization problems. We review the various meta-heuristics that have been specifically developed to solve lot sizing problems, discussing their main components such as representation, evaluation, neighborhood definition and genetic operators. Further, we briefly review other solution approaches, such as dynamic programming, cutting planes, Dantzig–Wolfe decomposition, Lagrange relaxation and dedicated heuristics. This allows us to compare these techniques. Understanding their respective advantages and disadvantages gives insight into how we can integrate elements from several solution approaches into more powerful hybrid algorithms. Finally, we discuss general guidelines for computational experiments and illustrate these with several examples.     

A dynamic lot sizing model with exponential machine breakdowns

This paper addresses the dynamic lot sizing model with the assumption that the equipment is subject to stochastic breakdowns. We consider two different situations. First we assume that after a machine breakdown the setup is totally lost and new setup cost is incurred. Second we consider the situation in which the cost of resuming the production run after a failure might be substantially lower than the production setup cost. We show that under the first assumption the cost penalty for ignoring machine failures will be noticeably higher than in the classical lot sizing case with static demand. For the second case, two lot sizes per period are required, an ordinary lot size and a specific second (or resumption) lot size. If during the production of a future period demand the production quantity exceeds the second lot size, the production run will be resumed after a breakdown and terminated if the amount produced is less than this lot size. Considering the results of the static lot sizing case, one would expect a different policy. To find an optimum lot sizing decision for both cases a stochastic dynamic programming model is suggested.     

Leximin population ethics

This paper concerns the ethical issues that arise when policy decisions have to be taken that affect population size and characteristics. Such policies include social security systems, intertemporal resource allocation decisions, and policies designed to influence fertility rates. The authors provide characterizations of Leximin principles for social evaluation in an intertemporal framework, so that they can be used to compare social alternatives with different population sizes. "The main axioms used in our characterizations are Hammond Equity together with Independence of the Utilities of the Dead (a plausible intertemporal consistency requirement) for the Critical-Level Leximin principles, and Positional Leximin Consistency (an axiom that allows non-constant critical levels) for the Positional-Extension Leximin principle. The performance of these principles is compared in the pure population problem and we argue that the Critical-Level Leximin principles are ethically more attractive than Positional-Extension Leximin."     

Optimal buffer allocation in tandems of last come first served queues

A large fixed number of buffer spaces is given. We consider the problem of allocating these spaces among the nodes of a tandem of last-come-first-served queues with general service time distributions and Poisson external arrivals so as to optimize some performance criterion associated with the time to buffer overflow, such as maximizing its mean or maximizing the probability that it exceeds some value. Consider the following rule of thumb: allocate the buffer spaces in inverse proportion to the logarithms of the effective service rates at the nodes. Here effective service rate denotes the ratio of the service rate to the stationary arrival rate. We prove that this rule of thumb achieves a nearly optimal buffer allocation under the assumption that the service time distributions satisfy an exponential tail condition. This problem has been studied earlier in the context of Jackson networks, where it was shown that the same rule of thumb achieves an allocation that is close to optimal. The technique of proof here is similar, but there are important differences. Both Jackson networks and the LCFS tandems considered here are product form networks (with infinite buffers). Optimism should lead us to expect that the near optimality of this rule of thumb holds much more generally for product-form networks, but this remains a conjecture at present.Research supported by NSF under NCR 8857731, by AT&T, and by Bellcore Inc.Research supported by IBM under a graduate fellowship.     

On optimal batch sizing in a multi-stage production system with rework consideration

Recently, Sarker et al. [Sarker, B.R., Jamal, A.M.M., Mondal, S., 2008. Optimal batch sizing in a multi-stage production system with rework consideration. European Journal of Operational Research 184(3) 915–929] presented an EPQ inventory model for a multi-stage manufacturing system with rework process; basically they proposed two operational inventory policies. In the paper, there are some mathematical expressions which are to be corrected. At first, this paper presents the mathematical expressions corrected and the appropriate solution to the numerical example. We also established the closed forms for the optimal total inventory cost, the conditions for which there is an optimal solution, and the mathematical expressions for determining the total additional cost for working with a non optimal solution for both policies that were not given by Sarker et al. (2008).     

Multi-item inventory control with full truckloads: A comparison of aggregate and individual order triggering

In this paper, we consider the stochastic joint replenishment problem in an environment where transportation costs are dominant and full truckloads or full container loads are required. One replenishment policy, taking into account capacity restrictions of the total order volume, is the so-called QS policy, where replenishment orders are placed to raise the individual inventory positions of all items to their order-up-to levels, whenever the aggregate inventory position drops below the reorder level. We first provide a method to compute the policy parameters of a QS policy such that item target service levels can be met, under the assumption that demand can be modeled as a compound renewal process. The approximation formulas are based on renewal theory and are tested in a simulation study which reveals good performance. Second, we compare the QS policy with a simple allocation policy where replenishment orders are triggered by the individual inventory positions of the items. At the moment when an individual inventory position drops below its item reorder level, a replenishment order is triggered and the total vehicle capacity is allocated to all items such that the expected elapsed time before the next replenishment order is maximized. In an extensive simulation study it is illustrated that the QS policy outperforms this allocation policy since it results in lower inventory levels for the same service level. Although both policies lead to similar performance if items are identical, it can differ substantially if the item characteristics vary.     

Hybrid heuristics for the capacitated lot sizing and loading problem with setup times and overtime decisions

The capacitated lot sizing and loading problem (CLSLP) deals with the issue of determining the lot sizes of product families/end items and loading them on parallel facilities to satisfy dynamic demand over a given planning horizon. The capacity restrictions in the CLSLP are imposed by constraints specific to the production environment considered. When a lot size is positive in a specific period, it is loaded on a facility without exceeding the sum of the regular and overtime capacity limits. Each family may have a different process time on each facility and furthermore, it may be technologically feasible to load a family only on a subset of existing facilities. So, in the most general case, the loading problem may involve unrelated parallel facilities of different classes. Once loaded on a facility, a family may consume capacity during setup time. Inventory holding and overtime costs are minimized in the objective function. Setup costs can be included if setups incur costs other than lost production capacity. The CLSLP is relevant in many industrial applications and may be generalized to multi-stage production planning and loading models. The CLSLP is a synthesis of three different planning and loading problems, i.e., the capacitated lot sizing problem (CLSP) with overtime decisions and setup times, minimizing total tardiness on unrelated parallel processors, and, the class scheduling problem, each of which is NP in the feasibility and optimality problems. Consequently, we develop hybrid heuristics involving powerful search techniques such as simulated annealing (SA), tabu search (TS) and genetic algorithms (GA) to deal with the CLSLP. Results are compared with optimal solutions for 108 randomly generated small test problems. The procedures developed here are also compared against each other in 36 larger size problems.     

Hybrid heuristics for the multi-stage capacitated lot sizing and loading problem

The multi-stage capacitated lot sizing and loading problem (MCLSLP) deals with the issue of determining the lot sizes of product items in serially-arranged manufacturing stages and loading them on parallel facilities in each stage to satisfy dynamic demand over a given planning horizon. It is assumed that regular time capacity decisions have already been made in the tactical level and allocated to the stages, but it is still an important decision problem whether to augment regular time capacity by overtime capacity. Each item may be processed on a technologically feasible subset of existing facilities with different process and setup times on each facility. Since the solution of the MCLSLP requires the design of a powerful algorithm, simulated annealing (SA) and genetic algorithms (GA) are integrated to enhance their individual performances. Furthermore, these global optimisation methods are incorporated into a Lagrangean relaxation scheme, hence creating a hybrid solution methodology. Numerical results obtained using these methods confirm the mutual benefits of integrating different solution techniques.     

Using mixture‐amount modeling to optimize the advertising media mix and quantify cross‐media synergy for specific target groups

One of the critical decisions in media planning is how to allocate advertising efforts across different media. While studies indicate that marketers can create positive synergy effects by spreading their effort across several media, there is little understanding of how much should be invested in each specific medium to optimize advertising results. In this study, we apply a novel methodology, mixture‐amount modeling, which allows advertisers to determine the optimal allocation of advertising effort across media as a function of the total advertising effort. Moreover, we test how the optimal allocation and the resulting response change for consumers with distinctive media usage patterns and varying degrees of product category experience. Based on these results, we quantify the potential synergy between media and calculate the synergistic capacity for specific target groups. We apply the model to data from 52 beauty care advertising campaigns that ran on TV and in magazines in the Netherlands and Belgium. We determine the optimal allocation of advertising investments (measured through Gross Rating Points) to maximize campaign recognition. Our findings support the existence of positive synergistic effects between magazine and TV advertising and illustrate that these effects depend on consumers' media usage and product category experience.     

The capacitated maximal covering location problem with backup service

The maximal covering location problem has been shown to be a useful tool in siting emergency services. In this paper we expand the model along two dimensions — workload capacities on facilities and the allocation of multiple levels of backup or prioritized service for all demand points. In emergency service facility location decisions such as ambulance sitting, when all of a facility's resources are needed to meet each call for service and the demand cannot be queued, the need for a backup unit may be required. This need is especially significant in areas of high demand. These areas also will often result in excessive workload for some facilities. Effective siting decisions, therefore, must address both the need for a backup response facility for each demand point and a reasonable limit on each facility's workload. In this paper, we develop a model which captures these concerns as well as present an efficient solution procedure using Lagrangian relaxation. Results of extensive computational experiments are presented to demonstrate the viability of the approach.     

Solution procedures for sizing of warehouses

In the past, researchers presented a linear programming formulation for the economic sizing of warehouses when demand is highly seasonal and public warehouse space is available on a monthly basis. The static model was extended for the dynamic sizing problem in which the warehouse size is allowed to change over time. By applying simplex routine, the optimal size of the warehouse to be constructed could be determined. In this paper, an alternative and simple method of arriving at an optimal solution for the static problem is given. Three extensions of the static model are given. These extensions involve costs varying over time, economies of scale in capital expenditure and/or operating cost and stochastic version. The dynamic warehouse sizing problem is shown to be a network flow problem which could be solved by using network flow algorithms. The structure of an optimal solution is also given. The concave cost version of the dynamic warehouse sizing problem is also discussed and it is shown that this problem can be solved efficiently using dynamic programming.     

Social utility functions for strategic decisions in probabilistic voting models

This paper studies societies which have probabilistic voting that is smooth, scalable and unbiased. Its results establish that, in such societies, the decisions of vote-seeking candidates who start at a common location (such as the status quo for the society's policies and/or the same allocation of campaign resources) contain implicit rationality properties. In particular, it shows that in every such society there exist social utility functions which simultaneously rationalize the directional Nash behavior of candidates, the stationary electoral equilibria, and the non-degenerate local electoral equilibria which can occur at these locations. This is shown to be true both for unconstrained and for constrained sets of possible candidate locations. An example of such a utility function (which occurs in every one of the societies under consideration) is also provided.     

Heterogeneity and increasing returns may drive socio-economic transitions

There are clear benefits associated with a particular consumer choice for many current markets. For example, as we consider here, some products might carry environmental or ‘green’ benefits. Some consumers might value these benefits while others do not. However, as evidenced by myriad failed attempts of environmental products to maintain even a niche market, such benefits do not necessarily outweigh the extra purchasing cost. The question we pose is, how can such an initially economically-disadvantaged green product evolve to hold the greater share of the market? We present a simple mathematical model for the dynamics of product competition in a heterogeneous consumer population. Our model preassigns a hierarchy to the products, which designates the consumer choice when prices are comparable, while prices are dynamically rescaled to reflect increasing returns to scale. Our approach allows us to model many scenarios of technology substitution and provides a method for generalizing market forces. With this model, we begin to forecast irreversible trends associated with consumer dynamics as well as policies that could be made to influence transitions.     

An optimisation approach to apparel sizing

A novel approach for the construction of apparel sizing systems is formulated. As a first step to this process, efficient sizing systems are defined based on a mathematical model of garment fit. Nonlinear optimisation techniques are then used to derive a set of possible sizing systems using multidimensional information from anthropometric data. The method is illustrated by developing a sizing system designed for a dress shirt of a military uniform using anthropometric data from the US Army. Results of this analysis show that endogenous size assignment and selection of disaccommodated individuals, together with relaxation of the requirement of a ‘stepwise’ size structure, results in substantial improvements in fit over an existing sizing system. The proposed methodology enables the development of sizing systems that can either increase accommodation of the population, reduce the number of sizes in the system, or improve overall fit in accommodated individuals.