Research on warehouse operation: A comprehensive review

An extensive review on warehouse operation planning problems is presented. The problems are classified according to the basic warehouse functions, i.e., receiving, storage, order picking, and shipping. The literature in each category is summarized with an emphasis on the characteristics of various decision support models and solution algorithms. The purpose is to provide a bridge between academic researchers and warehouse practitioners, explaining what planning models and methods are currently available for warehouse operations, and what are the future research opportunities.     

Production planning with discounting and stochastic demands

A general continuous review production planning problem with stochastic demand is considered. Conditions under which the stochastic problem may be correctly solved using an equivalent deterministic problem are developed. This deterministic problem is known to have the same solution as the stochastic problem. Moreover, conditions are established under which the deterministic equivalent problem differs from a commonly used deterministic approximation to the problem only in the interest rate used in discounting. Thus, solving the stochastic problem is no more difficult than solving a commonly used approximation of the problem.     

Production planning with load dependent lead times

Lead times impact the performance of the supply chain significantly. Although there is a large literature concerning queuing models for the analysis of the relationship between capacity utilization and lead times, and there is a substantial literature concerning control and order release policies that take lead times into consideration, there have been only few papers describing models at the aggregate planning level that recognize the relationship between the planned utilization of capacity and lead times. In this paper we provide an in-depth discussion of the state-of-the art in this literature, with particular attention to those models that are appropriate at the aggregate planning level. Received: September 2005 / Revised version: November 2005 AMS classification: 46N10 All correspondence to: Stefan Vo?     

Production planning with load dependent lead times: an update of research

Lead times impact the performance of the supply chain significantly. Although there is a large body of literature concerning queuing models for the analysis of the relationship between capacity utilization and lead times, and another body of work on control and order release policies that take lead times into consideration, there have been relatively few aggregate planning models that recognize the (nonlinear) relationship between the planned utilization of capacity and lead times. In this paper we provide an in-depth discussion of the state-of-the art in this area, with particular attention to those models that are appropriate at the aggregate planning level. An earlier version of this paper appeared in 4OR 3, 257–302, 2005.     

Integrating deterioration and lifetime constraints in production and supply chain planning: A survey

Items with short lifetimes that are subject to deterioration are important in the business world. Research has a long tradition in integrating deterioration and value loss effects into mathematical models for inventory planning and control where such effects are understood as a general loss or shrinkage of inventory. However, there has been little work in the modeling of lifetime restrictions of items to prevent wastage and disposals, especially in a dynamic planning context. Globalization and other trends extend the consideration of single companies to whole supply chains, implying increased coordination and information needs. This is important as planning decisions impact lead times and thus the quality of items in the whole supply chain. Products that exceed their useful lifetime can impose high costs due to inventory loss or the need to rework them. This implies increased utilization of (scarce) resources, e.g., machine time, metals, and/or energy, thereby increasing CO₂${\text{CO}}_2$-levels. We survey the state-of-the-art regarding depreciation effects and the modeling of lifetime constraints as well as a classification of models following business planning functions of the value chain. A critical evaluation of approaches and their limitations is provided, highlighting directions for future research.     

Collaborative production planning of supply chain under price and demand uncertainty

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.     

Capacity planning and warehouse location in supply chains with uncertain demands

We discuss the strategic capacity planning and warehouse location problem in supply chains operating under uncertainty. In particular, we consider situations in which demand variability is the only source of uncertainty. We first propose a deterministic model for the problem when all relevant parameters are known with certainty, and discuss related tractability and computational issues. We then present a robust optimization model for the problem when the demand is uncertain, and demonstrate how robust solutions may be determined with an efficient decomposition algorithm using a special Lagrangian relaxation method in which the multipliers are constructed from dual variables of a linear program.     

Research on warehouse design and performance evaluation: A comprehensive review

This paper presents a detailed survey of the research on warehouse design, performance evaluation, practical case studies, and computational support tools. This and an earlier survey on warehouse operation provide a comprehensive review of existing academic research results in the framework of a systematic classification. Each research area within this framework is discussed, including the identification of the limits of previous research and of potential future research directions.     

The capacity planning problem in make-to-order enterprises

This paper addresses the short-term capacity planning problem in a make-to-order (MTO) operation environment. A mathematical model is presented to aid an operations manager in an MTO environment to select a set of potential customer orders to maximize the operational profit such that all the selected orders are fulfilled by their deadline. With a given capacity limit on each source for each resource type, solving this model leads to an optimal capacity plan as required for the selected orders over a given (finite) planning horizon. The proposed model considers regular time, overtime, and outsourcing as the sources for each resource type. By applying this model to a small MTO operation, this paper demonstrates a contrast between maximal capacity utilization and optimal operational profit.     

Design and control of warehouse order picking: A literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for the warehouse, and consequently for the whole supply chain. In order to operate efficiently, the order-picking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.     

Production planning and pricing policy in a make-to-stock system with uncertain demand subject to machine breakdowns

We consider a make-to-stock system served by an unreliable machine that produces one type of product, which is sold to customers at one of two possible prices depending on the inventory level at the time when a customer arrives (i.e., the decision point). The system manager must determine the production level and selling price at each decision point. We first show that the optimal production and pricing policy is a threshold control, which is characterized by three threshold parameters under both the long-run discounted profit and long-run average profit criteria. We then establish the structural relationships among the three threshold parameters that production is off when inventory is above the threshold, and that the optimal selling price should be low when inventory is above the threshold under the scenario where the machine is down or up. Finally we provide some numerical examples to illustrate the analytical results and gain additional insights.     

Calendarization of time planning in MPM networks

A calendar contains working days and non-working days. How to make time planning for a project, when some activities cannot be interrupted by non-working days or in some time distances between start times of activities also non-working days count, is a problem often facing us when undertaking a project realization. In this paper an exact method to solve this problem is presented and the optimality of this method is proved.     

Flexible long-term capacity planning in closed-loop supply chains with remanufacturing

We deal with long-term demand-driven capacity planning policies in the reverse channel of closed-loop supply chains (CLSCs) with remanufacturing, under high capacity acquisition cost coupled with uncertainty in actual demand, sales patterns, quality and timing of end-of-use product returns. The objective is to facilitate the decision-making when the management faces the dilemma of implementing either a strategy of early large-scale investments to benefit from economies of scale and capacity readiness, or a flexible strategy of low volume but more frequent capacity expansions. We consider a CLSC with two sequential product-types. We study the system’s response in terms of transient flows, actual/desired capacity level, capacity expansions/contractions and total supply chain profit, employing a simulation-based system dynamics optimization approach. Extensive numerical investigation covers a broad range of real-world remanufacturable products under alternative scenarios in relation to the market preference over product-types. The key findings propose flexible policies as improved alternatives to large-scale capacity expansions/contractions in terms of adaptability to the actual pattern of end-of-use product returns and involved risk in the investments’ turnover. Flexible policies are also proposed as practices to avoid overcapacity phenomena in collection and remanufacturing capacity and as robust policies to product demand. Their implementation is revealed to be even more important for the case of remanufacturing, when a high capacity acquisition unit-cost ratio (remanufacturing/collection) is coupled with strong economies of scale. Finally, results under different information sharing structures show changes in remanufacturing policies, thus justifying the importance of coordination between the decision-maker and the distributor.     

Environmental safety stock: The impacts of regulatory and voluntary control policies on production planning,inventory control,and environmental performance

This paper analyzes the impacts of different pollution control policies on a firm’s decisions of production planning and inventory control. Based on a stochastic model with both demand and environmental uncertainties, we derive the optimal policies of production planning and inventory control under both regulatory and voluntary pollution control approaches, and investigate their operational and environmental effects. We establish that the conventional wisdom which suggests that reduction of environmental waste at the end of a production process also decreases the stock and throughput levels of a production system is not necessarily true. Rather, a regulatory environmental standard that limits the total amount of waste may induce the firm to raise its planned stock level, which would lead to a higher expected amount of environmental wastes before the standard is enforced as well as environmental risks at other stages of the production process. The additional planned stock level, which is termed “environmental safety stock,” can be reversed by using the voluntary control approach that provides the firm with the flexibility to occasionally exceed the environmental standard. We also conduct numerical experiments to analyze the effects of different values of model parameters under different control approaches. The analytical results provide new insights to the impacts of a firm’s production and inventory decisions on the natural environment as well as to the choices of pollution control approaches by decision makers in both the private and public sectors.     

Aggregate-level demand management in evacuation planning

Without successful large-scale regional evacuations, threats such as hurricanes and wild-fires can cause a large loss of life. In this context, automobiles are oftentimes an essential transportation mode for evacuations, but the ensuing traffic typically overwhelms the roadway capacity and causes congestion on a massive scale. Congestion leads to many problems including longer, costlier, and more stressful evacuations, lower compliance rates, and increased risk to the population. Supply-based strategies have traditionally been used in evacuation planning, but they have been proven to be insufficient to reduce congestion to acceptable levels. In this paper, we study the demand-based strategies of aggregate-level staging and routing to structure the evacuation demand, both with and without congestion. We provide a novel modeling framework that offers strategic flexibility and utilizes a lexicographic objective function that represents a hierarchy of relevant evacuation-based goals. We also provide insights into the nature and effect of network bottlenecks. We compare our model with and without congestion in relation to tractability, normative optimality, and robustness under demand uncertainty. We also show the effectiveness of using demand-based strategies as opposed to using the status quo that involves a non-staged or simultaneous evacuation process. Effective solution procedures are developed and tested using hypothetical problem instances as well as using a larger study based on a portion of coastal Virginia, USA.     

Group technology in production management: The short horizon planning level

We consider a set of parts divided into subsets called part types, determined in such a way that the parts belonging to the same part type are manufactured using the same sequence of tasks (i.e. the same working process). We are looking for a partition of the set of part types into subsets called part families, and for a partition of the set of tasks into subsets called production subsystems defined as follows: (1) the number of part families and the number of production subsystems are equal, (2) one (one only one) production subsystem corresponds to each part family, (3) one (and only one) part family corresponds to each production subsystem, (4) the previous partitions minimize the number of tasks performed in a production subsystem different from that which corresponds to the part family containing the part involved. We give a fast algorithm which leads to a good solution depending on the initial set of part families. We also propose an algorithm to find a ‘good’ initial set of part families.     

A mathematical model for the Multi-Levels Product Allocation Problem in a warehouse with compatibility constraints

The aim of this work is to address the products allocation problem in a multi-layers warehouse with compatibility constraints among the classes. The problem under study represents one of the most relevant topic in Logistics. The goal is to reduce, as much as possible, the delivery times; the inventories; the total logistic costs and to guarantee, at the same time, higher service levels (i.e., high customers satisfaction degree). In this work, a linear model to mathematically represent the problem is developed and its performance is evaluated on a set of instances, representing realistic situations. A sensitivity analysis is also carried out by considering the most relevant parameters of the model. Finally, an Iterated Local Search based heuristic is defined in order to solve large scale scenarios in a reasonable amount of time. Numerical results show that the proposed heuristic is able to find good quality solutions with a computational effort lower than that required to solve the proposed mathematical model.     

Capacity planning in networks of queues with manufacturing applications

This paper addresses capacity planning in systems that can be modeled as a network of queues. More specifically, we present an optimization model and solution methods for the minimum cost selection of capacity at each node in the network such that a set of system performance constraints is satisfied. Capacity is controlled through the mean service rate at each node. To illustrate the approach and how queueing theory can be used to measure system performance, we discuss a manufacturing model that includes upper limits on product throughput times and work-in-process in the system. Methods for solving capacity planning problems with continuous and discrete capacity options are discussed. We focus primarily on the discrete case with a concave cost function, allowing fixed charges and costs exhibiting economies of scale with respect to capacity to be handled.     

A detailed workforce planning model including non-linear dependence of capacity on the size of the staff and cash management

This paper introduces an original planning model which integrates production, human resources and cash management decisions, taking into account the consequences that decisions in one area may have on other areas and allowing all these areas to be coordinated. The most relevant characteristics of the planning problem are: (1) production capacity is a non-linear function of the size of the staff; (2) firing costs may depend on the worker who is fired; (3) working time is managed under a working time account (WTA) scheme, so positive balances must be paid to workers who leave the company; (4) there is a learning period for hired workers; and (5) cash management is included. A mixed integer linear program is designed to solve the problem. Despite the size and complexity of the model, it can be solved in a reasonable time. A numerical example, the main results of a computational experiment and a sensibility analysis illustrate the performance and benefits of the model.