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The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks.

This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time.

Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window.

The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies.

For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

A heuristic algorithm, designed for freeze‐drying operations in the food processing industry, is developed for a multistage system with parallel machines and resource constraints. The system is extremely complex owing to factors such as stage capacities, resource constraints and requirements, set‐up charges, product mix, order sizes, and co‐ordination of multiple stages. The primary objectives are meeting due dates, minimizing workflow time, and maximizing equipment utilization. The system is implemented to generate production schedules in a microcomputer‐based operating environment.

Reports a new method for preparing a product structure analysis to improve the effectiveness of the master scheduling function for products that are manufactured on an assemble‐to‐order basis. This methodology for conducting product structure analysis uses relational database management software to identify common and unique material in a product structure. Highlights example results of the application of methodology.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

Since the early 1970s, production planning systems have evolved from material requirements planning (MRP) through manufacturing resource planning (MRPII) into enterprise resource planning (ERP) with simultaneous development of related control systems such as theory of constraints (epitomised by OPT), just‐in‐time (JIT), etc. One key area for all manufacturing companies is the planning and control function. There is a wide range of generic proprietary software available that aims to meet a company’s planning and scheduling requirements. The difficulty experienced by many companies is not only in examining available software, but also in understanding the match between business needs and the capabilities of that software. This paper first sets out some common manufacturing classification systems, then attempts to map them against accepted paradigms for production planning and control approaches. Analysis confirms the need for a more rigorous approach to software selection, and the need for a complete understanding of the drivers of the production control process before this can be achieved. The paper goes on to discuss a method for mapping these drivers, with the aim being to create a series of reference models for production planning and scheduling.

The purpose of this paper is to propose a robust optimization approach for the short‐term scheduling of batch plants under demand uncertainty where the uncertain parameters can be described by a normal distribution function.

The robust optimization formulation introduces a small number of auxiliary variables and additional constraints into the original mixed integer linear programming problem, generating a deterministic robust counterpart problem which provides the optimal solution given the magnitude of the uncertain data, a feasibility tolerance, and a reliability level.

Developed robust optimization approaches produce robust solutions for uncertainties in both the coefficients and right‐hand‐side parameters of the linear inequality constraints and can be applied to address the problem of production scheduling with uncertain parameters.

The choice of the magnitude of the uncertain data, a feasibility tolerance, and a reliability level are the main limitation of the model.

Very useful advice for short‐term scheduling of batch plants under demand uncertainty.

The paper proposes a robust optimization approach for short‐term scheduling of batch plants under demand uncertainty. Computational results are presented to demonstrate the effectiveness of the proposed approach.

Takes the development of a new optimization process for production scheduling and develops it into a systematic‐mathematical algorithm. Tests this algorithm against a simulated production environment and compares the generated schedules against those generated by EOQ, MRP, JIT, and OPT. The result is that bottleneck allocation methodology (BAM), with its critical resource based capacity scheduling out‐performs these other models in an intermittent demand discrete manufacturing environment.

An attempt is made to classify the lot‐sizing problem based on evidence from the literature and current research trends. For future research a mixture of a heuristic method to find a sequence and cycle time and a mathematical program to find lot sizes would be feasible even for fairly large problems. Attempts should be made to apply marginal analysis in practical lot‐sizing problems since it may result in lower cost solutions.

An examination of changing an inventory policy of a major manufacturing organisation and its impact on the cost structure of the organisation is presented. A classic cost confrontation between set‐up costs and inventory costs are examined in the study. The results reveal that the unique nature of the manufacturing organisation favours short‐run production scheduling over a proposed long‐run production scheduling policy. This article also presents the application of a decision support system (DSS) to aid in production scheduling. The applications reveals that improved scheduling and a reduction in scheduling time and effort can be achieved by using the DSS over the manufacturing organisations′ manual systems.