Capacitated lot-sizing with sequence dependent setup costs

In this paper we consider a single-stage system where a number of different items have to be manufactured on one machine. Expenditures for the setups depend on the sequence in which items are scheduled on the machine. Holding costs are incurred for holding items in inventory. The demand of the items has to be satisfied without delay, i.e. shortages are not allowed. The objective is to compute a schedule such that the sum of holding and setup costs is minimized with respect to capacity constraints. For this problem which we call capacitated lot-sizing problem with sequence dependent setup costs (CLSD) we formulate a new model. The main differences between the new model and the discrete lot-sizing problem with sequence dependent setup costs (DLSDSD), introduced by Fleischmann, is that continuous lot-sizes are allowed and the setup state can be preserved over idle time. For the solution of the new model we present a heuristic which applies a priority rule. Since the priority values are affected by two significant parameters, we perform a local search in the parameter space to obtain low cost solutions. The solution quality is analyzed by a computational study. The comparison with optimal solutions of small instances shows that the solution quality of our heuristic is acceptable. The Fleischmann approach for the DLSPSD computes upper bounds for our new problem. On the basis of larger instances we show that our heuristic is more efficient to solve the CLSD.     

Evaluating artificial intelligence heuristics for a flexible Kanban system: simultaneous Kanban controlling and scheduling

This paper evaluates several artificial intelligence heuristics for a simultaneous Kanban controlling and scheduling on a flexible Kanban system. The objective of the problem is to minimise a total production cost that includes due date penalty, inventory, and machining costs. We show that the simultaneous Kanban controlling and scheduling is critical in minimising the total production cost (approximately 30% cost reduction over scheduling without a Kanban controlling). To identify the most effective search method for the simultaneous Kanban controlling and scheduling, we evaluated widely known artificial intelligence heuristics: genetic algorithm, simulated annealing, tabu search, and neighbourhood search. Computational results show that the tabu search performs the best in terms of solution quality. The tabu search also requires a much less computational time than the genetic algorithm and the simulated annealing. To further improve the solution quality and computational time for a simultaneous Kanban controlling and scheduling on a flexible Kanban system, we developed a two-stage tabu search. At the beginning of the tabu search process, an initial solution is constructed by utilising the customer due date information given by a decision support system. The two-stage tabu search performs better than the tabu search with a randomly generated initial solution in both the solution quality and computational time across all problem sizes. The difference in the solution quality is more pronounced at the early stages of the search.     

Particle swarm optimization for the multi-period cross-docking distribution problem with time windows

Cross-docking has emerged as a new technique in supply chain management to replace the warehouse concept in the retail industry. This paper proposes a multi-period cross-docking distribution problem that consists of manufacturers, cross-docks and customers. This model is formulated for cases that consider multiple products, consolidation of customer orders and time windows that are available in multiple periods. The objective function is to minimise the total cost, which includes transportation cost, inventory cost and penalty cost. The penalty cost arises when demand remains in each period that cannot be satisfied. To deal with the complexity of the problem, an algorithm is developed based on particle swarm optimisation (PSO) with multiple social learning terms, GLNPSO, with two solution representations. The solution representations are a one-period solution representation (OP-SR) and a multi-period solution representation (MP-SR). The GLNPSO-based algorithm performs well in solving this problem. Moreover, both representations are proven effective when comparing the solution quality and computational time with those results obtained from CPLEX. In terms of quality, the MP-SR solution is better than the OP-SR solution for both stable and fluctuating demand instances. However, MP-SR requires more computational effort than OP-SR.     

Economic Design of CSP-1 Plan Under the Dependent Production Process and Linear Inspection Cost

Chen and Chou presented the economic design of type I continuous sampling plan (CSP-1 plan) under the linear inspection cost. However, it can be argued that the production process is seldom independent. In this paper, we further propose the problem concerning the economic design of CSP-1 plan under the dependent production process and linear inspection cost. A solution procedure is developed to find the unique combination (i*, f*) that will meet the average outgoing quality limit (AOQL) requirement while also minimizing the total expected cost per unit produced during one inspection cycle.     

Framework for flexibility in dynamic supply chain management

Effective Supply Chain Management (SCM) is a pivotal issue in the dynamically changing business environment. Dynamic SCM requires integrated decision-making amongst autonomous chain partners with effective decision information synchronization amongst them. By exploiting flexibility in supply chain structures, better performance can be achieved. Similarly, by judiciously employing decision flexibility and the associated dynamic control amongst autonomous supply chain nodes, many improvements are possible. The paper presents a study on the role of different flexibility options (i.e. no flexibility, partial flexibility and full flexibility) in a dynamic supply chain model based on some key parameters and performance measures. Supply chain flexibility has significant potential and needs a greater research attention. The paper attempts to advance the knowledge of dynamic control on effective flexibility exploitation in the context of dynamic supply chains. A simulation model of a dynamic supply chain is used for this purpose. The key results are highlighted along with industry implications. Here each supply chain node involves decision-making. Based on the order and sample information available from the immediate buyers or customers, the supplier selection decisions are dynamically made. A seemingly good decision at a stage based on local information often ends up as detrimental not only to the total chain cost, but also to the total costs of the node itself. These observations are important for the designers and managers of the flexible supply chain systems to arrive at appropriate types and a judicious level of flexibility to attain significant improvements in total cost reduction. The modelling of dynamic supply chains with a focus on flexibility can offer enormous potential to the industry. This paper addresses this interesting and challenging domain.     

A vehicle routing problem with time windows and stochastic demands

Abstract

This study presents an approach for considering a vehicle routing problem where customers’ pickup demands are uncertain and require serving within some settled time windows. Customers’ demands are assumed to follow given discrete probability distributions. This study proposes a nonlinear stochastic integer program with recourse to formulate the vehicle routing problem with stochastic demands and time windows (VRPTW‐SD, for short). The objective of the VRPTW‐SD is to minimize the total cost of the first‐stage solution and expected recourse cost of the second‐stage solution. The total cost of the first‐stage problem includes the total travel cost for all links and the total waiting cost at all nodes. When a vehicle capacity is attained or exceeded, recourse actions are needed and recourse costs incurred in order to finish the planned route schedules. Two categories of schedule failure are introduced in this work; the recourse costs derive from the variations in travel time travel time, waiting time, and penalties of late arrival for time windows. In addition, an optimization algorithm is developed for solving the VRPTW‐SD, according to the framework of the L‐shaped method. Numerical results are given to demonstrate its validity.     

Impact on inventory costs with consolidation of distribution centers

The consolidation of Distribution Centers (DCs) is a new trend in global logistics management, with a reduction in inventory costs often being cited as one of the main benefits. This paper uses an analytical modeling approach to study the impact on facility investment and inventory costs when several DCs are consolidated into a central DC. In particular, our model suggests that consolidation leads to lower total facility investment and inventory costs if the demands are identically and independently distributed, or when they follow independent but possibly nonidentical Poisson processes. This agrees with the conclusion of the classical EOQ and newsvendor models. However, we show by an example that, for general stochastic demand processes, the total facility investment and inventory costs of a consolidated system can be infinitely worse off than that of a decentralized system. This arises mainly because the order replenishment fixed cost yields a cost component proportional to the square root of the mean value of the demand, while the demand uncertainty yields a cost component proportional to the standard deviation of the demand. Whether consolidation is cost effective or not depends on the trade-off between these two components, as indicated by an extensive numerical study. We also propose an algorithm that solves for a distribution system with the total facility investment and inventory costs within √2 of the optimal.     

Integration of vendor selection into production and remanufacturing planning subject to emission constraints

This paper presents a new model formulation that incorporates vendor selection into production and remanufacturing planning subject to emission constraints. The objective is to determine a feasible production and remanufacturing plan at minimal cost. In each period, the given external demand must be satisfied for both new and remanufactured products. Remanufactured products can be substituted by new products, but not vice versa. When a product is (re)manufactured, the appropriate machine must be set up, which entails set-up costs and/or set-up time. The procurement of raw material that is offered by several vendors at different prices is integrated into this planning problem. To solve this integrated production and remanufacturing planning problem, we apply two solution approaches based on mathematical programming: first, a combination of column generation and a period-oriented fix-and-relax heuristic and second, an adapted fix-and-optimise heuristic. The results of our numerical investigation demonstrate the high solution quality of both solution approaches.     

Drivers,barriers and social considerations for AI adoption in SCM

This study explored the challenges and recommendations for implementing Artificial Intelligence (AI) in SC Management (SCM). The experts identified several drivers for AI adoption in SCM, including increased efficiency, improved decision-making, and reduced costs. However, they highlighted several barriers to AI adoption, such as data quality and management issues, resistance to change, and lack of understanding and trust in AI. To overcome these barriers and ensure successful AI implementation, companies should involve all stakeholders, focus on data quality and management, and ensure the AI solution integrates with existing processes and workflows. In addition, companies should also avoid common mistakes when implementing AI, such as neglecting the importance of explainability and transparency in AI decision-making, underestimating the importance of involving all stakeholders, and rushing into large-scale implementation without conducting small-scale pilot projects. By following the recommendations and avoiding common mistakes, companies can harness the benefits of AI in SCM while minimizing risks and challenges.     

Application of Grey System Theory in Design For Cost （DFC）

Design For Cost (DFC) is a branch of Design For X (DFX). In this paper, we defined DFC as a design method that analyzed and evaluated the product‘s life cycle cost (LCC), then modified the design to reduce the LCC. Nowadays it is a very difficult thing to obtain LCC data in China or in developing countries. Statistical methods can not be used because available LCC data are few. In order to solve this problem, we used grey system theory. Then relations of cost factors were analyzed in LCC using grey relevant methods, and a GM( 1,1 ) model between design parameters and LCC was established. Using this model, we can estimate and control LCC by changing design parameters at the beginning of the design stage.